Weekly AI digest
Radiology & medical imaging AI · week of 6 to 12 September 2026
185 peer-reviewed papers · 4 industry and regulatory items · conference and KOL highlights. Adapted from my weekly intelligence report. Full report: FR (PDF) · EN (PDF) · RU (PDF).
Governance takeaway
What to do about it this week
This week hands a radiology leader a clean two-way split in evidence maturity. AI assistance for image interpretation now has trial-grade support: a five-centre randomised crossover in Lancet Digital Health shows an ultrasound aid raising sonographer sensitivity for fetal intracranial malformations by 0.087 while formally protecting specificity. LLM report generation has nothing comparable: a systematic review of 101 studies found not one at low risk of bias and safety data too heterogeneous to pool.
The action: split your AI pipeline into these two evidence classes. For interpretation aids, require reader-performance evidence of the kind that now demonstrably exists. For LLM drafting, treat every pilot as building the safety case from zero, on your own casemix, with your own error counts.
Regulation & AI Act watch
Epsilon Health emerges from stealth with 27.6 million dollars
Not a cleared device but a business model: an AI-native radiology practice that embeds its own AI into the workflow of its board-certified radiologists, claiming 2,500 studies daily and a path to 1 percent of US x-rays this year, on company statements rather than published performance data. The question to ask before contracting: which functions are FDA-cleared devices, and which are practice-of-medicine tools whose oversight lives only in the service-level agreement.LUMA Vision announces expanded FDA clearance for VERAFEYE
An expanded clearance for a 4D intracardiac imaging, visualization and navigation platform ahead of a 2027 commercial launch, announced without published clinical performance data for the new capabilities. The question to ask the vendor: the exact intended-use wording, and which guidance functions are automated on what validation data.AEYE Health publishes its pivotal trials for autonomous diabetic retinopathy screening
The three prospective controlled studies behind an FDA-cleared autonomous screening AI moved from regulatory files into the peer-reviewed literature, over 1,200 patients with one image per eye. The question to ask: what gradeability and false-positive rates look like on your own cameras and operators, since the workflow runs with no physician in the loop.Qscription and NEOPATHOLOGY sign an MOU for a cleared lung imaging AI
A preliminary partnership built on a candid premise: cleared AI routinely stalls between clearance and clinical use on integration, security review and onboarding. The announcement does not name the device; the first question is which clearance, verbatim, this concerns.
Evidence you can use
Selected from 185 papers indexed in PubMed this week.
Evaluating AI-assisted detection of fetal intracranial malformations in prenatal ultrasound practice: a multicentre, self-crossover, randomised controlled trial in China
The Lancet Digital Health, 10 September 2026Abdominal landmark detection and classification of fetal growth conditions on obstetric ultrasound: a gestational age-conditioned multi-task network
Abdominal Radiology, 10 September 2026Resolution-dependent self-supervised transfer in chest radiograph classification
Communications Medicine, 9 September 2026Automated Extraction of Postoperative Cancer Recurrence and Metastasis From Computed Tomography Reports: Semisupervised Deep Learning Study
JMIR Medical Informatics, 8 September 2026Development and Validation of a Convolutional Neural Network Framework Based on Ultrasound Imaging for Multi-Classification of Superficial Soft Tissue Masses
Academic Radiology, 7 September 2026Effectiveness, Safety, and Workflow Burden of Large Language Model-Based Medical Report Generation: Systematic Review
Journal of Medical Internet Research, 9 September 2026Global Research Trends and Insights on AI Usage in Tuberculosis: Bibliometric Analysis
JMIR Medical Informatics, 10 September 2026ParasiteNet: Deep Learning-Based Identification of Parasitic Helminth Eggs in Microscopic Images
Journal of Imaging Informatics in Medicine, 8 September 2026
Post-market signal
The drift gap between clearance and clinical reality moved into the trade press this week. An Imaging Technology News feature on AI sprawl quotes TestDynamics on the problem its vendor-neutral Satori platform monitors: FDA clearance reflects performance at a point in time, while local populations, scanners and protocols keep changing, so post-deployment drift belongs to the deployer. The metrics named, output drift and radiologist-AI agreement trended over time, are exactly the evidence stream an Article 72 deployment file needs, and declining reader agreement is often the earliest observable signal.
The same week quantified why this matters. A PLOS Digital Health analysis covered by TechTarget found that of more than 1,300 FDA-cleared AI devices, only 2.5 percent were linked to registered prospective trials and only three devices, 0.2 percent, were ever evaluated for patient-centered outcomes. Radiology holds 78 percent of cleared devices and 1 percent of the prospective trials. The burden of demonstrating real-world performance sits almost entirely on deployers, after purchase.
Playbook snippet
One step for your AI committee
Anchor: EU AI Act Article 9 (risk management), pre-deployment.
Step: Before any LLM or classifier pilot, assemble 100 consecutive routine cases from your own service, not teaching cases, and score the candidate tool on them before you see any vendor demo. Record the case list, the model version and the scores in the risk file.
Why: Curated demos systematically overstate: performance on teaching cases does not transfer to routine casemix, and this week's 101-study review shows the published literature cannot carry the safety case for you.
Worked example: Wu M et al. show why the local test matters even for well-built tools: ST-USNet, developed on four institutions with validation AUC 0.984 for malignancy, still performed measurably lower on an independent test cohort, and its authors call for prospective multi-center validation before deployment. Academic Radiology, PMID 42705922, DOI 10.1016/j.acra.2026.08.092.
From the field
The Harvey L. Neiman Health Policy Institute announced a JACR study from Northwell Health: a prospective shadow-mode evaluation of an FDA-cleared aneurysm algorithm across 3,856 CTA examinations. AI added 55 true-positive aneurysms radiologists had not reported, a 39 percent relative detection increase, at the cost of 46 false positives, while radiologists caught 30 aneurysms the AI missed. Performance varied sharply by care setting, favorable inpatient and marginal outpatient, which is the study's real lesson: evaluate AI on your own settings and keep monitoring after go-live.
RSNA published a Board-level update on integrating advanced practice providers into radiologist-led care teams, prioritizing education and convening while deferring standards-setting. The capacity pressure driving APP integration is the same business case behind most imaging AI purchases, which makes the two strategies worth planning together.
On LinkedIn, Bernardo Bizzo of the ACR Data Science Institute announced a new JACR paper describing the LLM engine inside Assess-AI, with a line that frames the season: the first wave of imaging AI produced a flag, the next wave will produce a draft report, and monitoring has to keep up. Amine Korchi dissected Vara's autonomous mammography CE certification and noted he could not find the certified intended-use text, a live demonstration of the first question to ask any autonomous AI vendor. Louis Blankemeier, amplified by Nina Kottler, argued that human and artificial intelligence are non-overlapping designs, the working rationale for complementarity rather than replacement.
Source articles are indexed in PubMed with verified DOIs. Manuscript-stage work is excluded. The full weekly report is produced in French.
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